Recent developments in the field of image processing, computer vision, and camera technologies have led to advancements in various image processing systems and techniques, such as object segmentation. A conventional object segmentation apparatus may segment an object-of-interest (such as a human body or human face) from an image of a scene, based on different object segmentation techniques. Examples of such object segmentation techniques may comprise, but is not limited to a compression based, a color-based, a depth-based, or a histogram based object segmentation technique.
Currently, in a distinctive color-based segmentation approach, the object-of-interest may be segmented based on subtraction of a pre-determined static background image from captured image. However, in order to generate the pre-determined static background image, the conventional object segmentation apparatus is required to capture a sequence of images of the scene when the object-of-interest is not present in the scene, which may not be desirable. The pre-determined static background image of the scene may be generated from the captured sequence of images. The background image subtraction approach may further require capture of the sequence of images from the scene with a static camera.
In another depth based approach, the conventional object segmentation apparatus may segment the object-of-interest by using a depth image which may be captured by a depth sensor. In cases where the depth sensor captures a noisy depth image comprising invalid depth values, the conventional object segmentation apparatus may segment the object-of-interest from the captured image erroneously and inaccurately.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.